Unsupervised POS Induction with Word Embeddings

Understand

Unsupervised word embeddings have been shown to be valuable as features in supervised learning problems; however, their role in unsupervised problems has been less thoroughly explored.

• In this paper, we show that embeddings can likewise add value to the problem of unsupervised POS induction.
• In two representative models of POS induction, we replace multinomial distributions over the vocabulary with multivariate Gaussian distributions over word embeddings and observe consistent improvements in eight languages.
• We also analyze the effect of various choices while inducing word embeddings on "downstream" POS induction results.